GSM, CDMA, and OFDM systems and those systems using space-time coding usually require estimation of impulse responses (IR's) between a terminal antenna and several base antennas, especially in instances when the relative signal strengths of the base stations are similar. As will be understood, the physical channel through which propagation occurs can have a severely detrimental effect on the ability to recover data accurately, especially with increasing data rates. Consequently, by knowing the IR, compensation for channel-induced defects can be applied to improve accuracy in data recovery, e.g. through interference cancellation techniques. More especially, with multiple transmit elements at a base station, irrespective of whether there is more than one serving base station, multipath interference results in an inability to resolve individual channels. Indeed, even orthogonally structured data (such as different and time dispersed training sequences) can become cross-correlated (in the face of multipath interference) and hence unusable to resolve individual channels/paths in the context of a deterministic IR approach. Furthermore, it will be appreciated that IR determination is particularly taxing at a handoff point at cell boundaries of, potentially, two or three serving base stations (or Node Bs in the context of CDMA and the Universal Mobile Telecommunications System (UMTS)). At cell-boundary handover, the issue of IR is complicated by the fact that downlink carrier to interference ratios (CIRs) can be zero or negative.
When available, impulse responses can also be used to assist in downlink soft-handover.
The use of matched filtering of standard training sequences to determine channel IR's is considered adequate to drive fingers of a RAKE filter in CDMA applications, such as IS-95 and CDMA2000, given that incoherent soft handoff is also allowed. However, more recent cellular system proposals will stretch matched filtering techniques to their limits and beyond due to the use of higher data throughputs (with more bits per symbol, e.g. higher quadrature amplitude modulation QAM levels) requiring higher energy per bit/noise floor (Eb/No) than existing systems, such as IS-95. Such higher data rate cellular systems include the Enhanced Data-rate for GSM Evolution (EDGE) system and multiple-input-multiple-output (MIMO) methods (using multiple transmit and receive antennas) applied to High Speed Downlink Packet Access (HSDPA) standards. MIMO methods potentially may also be applied to UTRA TDD, CDMA2000 and even GSM-EDGE.
In any event, interference cancellation, coherent soft hand-off or space-time coding generally requires a more accurate estimate of channel propagation conditions (than that required with RAKE filtering or matched filtering). In this respect, a long history of interference cancelling base station algorithms (that yield indifferent null-steering performance) lends support to this view.
In terms of proposed 3G (third generation) cellular systems, it is known to use training sequences with a cyclic prefix for pilot tones in orthogonal frequency division multiplexing (OFDM) and CDMA. When a channel has an impulse response of known maximum duration, the use of Steiner codes allows re-use of the same training sequence with a time-offset method to measure simultaneously the channel impulse responses of several users or several antenna with a single fast Fourier transform (FFT) operation Steiner codes are discussed in the paper “A comparison of uplink channel estimation techniques for MC/JD-CDMA transmission systems” by B. Steiner and R. Valentin, Proceedings IEEE 5th International Symposium on Spread Spectrum Techniques and Applications 1998, Volume: 2, pp. 640–646. Indeed, cyclic Steiner codes are found in the pilot tones for the time division duplex (TDD) component of European UMTS, as presented in the ETSI 3GPP Document TS25.211 “Transport channels and physical channels” v.3.2.0, 1999. In terms of a frame structure for a pilot tone, random data sectors sandwich an observation window that is preceded by a cyclic prefix of a latter portion of the observation window. Provided that the duration of the cyclic prefix exceeds the channel impulse response, then data in the observation window is a function purely of the pilot sequence (comprised from the combination of the cyclic prefix and data in the observation window) and is not corrupted by spurious unknown data in any way.
The 3G Partnership Project (3GPP) have defined training sequences for TDD time division duplex) having overall chip lengths of 256 and 512 chips; the overall lengths are derived from 192 chips and 456 chips of basic pseudo-random number sequence codes with, respectively, an additional cyclic prefix of either 64 chips and 56 chips. Each training sequences is designed to allow channel impulse response estimation for differing numbers of users, namely three users in the case of the 256-chip training sequence and eight users in the case of the 512-chip training sequence.
The training sequences are utilised in the receiving unit to estimate the channel impulse response based on a complex cross-correlation (in real and imaginary phase and amplitude components) between received chips and a local replica of the training sequence. In this regard, it is usual, on a per channel basis, to take a correlation of the channel impulse response (h) with the sequence (s) from the transmitter. More particularly, from a single base station having multiple transmit elements, cyclic offsetting of Steiner codes allows utilisation of a fast Fourier transform (FFT) technique to solve individual channel impulse responses. Steiner cyclic pilot codes can therefore be used in estimating, with a single correlator, channel impulse responses of multiple users that do not mutually interfere. Steiner codes may be Gold codes.
As will be understood, the requirement for cyclic redundancy to mitigate data corruption caused by multipath may be obviated provided that the length of the transmitted training sequence (or pilot tone) is sufficiently long. In generality, cyclic redundancy may be avoided if a training sequence contains a sufficient number of chips, with the overall length of the training sequence determined by the prevailing dispersion conditions associated with the channel. Additionally, for estimating CIR, it is also necessary to consider the physical state of the receiving unit, since the channel for a slow moving or stationary receiving unit (e.g. a handheld device) is ostensibly stable, i.e. constant.
By way of practical explanation of Steiner code operation from a dual element array of a base station transmitter, it will be appreciated that, in estimating the two channels from the two radiating elements to a single receive antenna, the two radiating elements use a common generic pseudorandom number (PN) training sequence {s1, s2} which is end-around shifted (in this case by half a block) to {s2, s1} for the second element. The two channel impulse responses are {h1, h2} and the receiver antenna sees the superposition of the two convolved sequences:{y}={s1,s2)}{circle around (x)}{h1}+{s2,S1}{circle around (x)}{h2}where {circle around (x)} means a discrete-time convolution operation. When the receiver output is correlated with the PN sequence of the first antenna, the impulse responses of the two distinct channels are recovered in superposed form with a relative time shift of half a block. If the maximum probable duration of the impulse responses is finite and known and less than half the PN code length, the two channel estimates are non-overlapping and orthogonal and can be recovered by slicing an output of a correlator block into two halves. Clearly the process extends easily to estimating M channels by splitting the generic PN sequence into M segments {s1, s2, . . . sM} where {s} means a short symbol sequence, and wherein transmissions from the base antenna transmit elements follow the sequences {s1, s2, . . . sM}, {sM, s1, . . . . {sM-1}sM, . . . sM-2}.
At the receive antenna, the processing block is correlated with only the first copy, {s1, s2, . . . sM} of the PN sequence whereupon, at the correlator output, the M different channel impulse responses separate out in time and appear in sequence. Clearly, the cyclic head is different for each downlink path, but this is purely a transmitter burden, whereas the receiver samples the waveform and continues to see the same rotated training sequence. In this way, a common discrete Fourier Transform operation can be used to resolve multiple downlink channel impulse responses.
As previously indicated, in order to avoid contamination with unknown data which usually immediately precedes the training sequence, a cyclic copy of the end of each rotated sequence may be prefixed at the start of the pilot block. In other words, when working with, say, 512-point processing blocks and when the duration of the channel impulse response is K samples, the total extent of the transmitted pilot burst is 512+K samples of which the first K contaminated samples are discarded at the receiver.
The maximum number M of channel impulse responses that can be estimated by the Steiner method is:
  M  ≤      N    K  where N is the length of the training sequence (samples) and K is the maximum likely number of samples in the channel impulse response.
Unfortunately, with Steiner codes, the resolution of base stations in different cells is much less attractive. Specifically, whilst it would be possible to continue to expand the Steiner method with ever increasing numbers of built in channels M (e.g. perhaps twice or three times the number needed for one base station), estimating more channels needs longer training sequences and synchronisation of the relevant base stations. Increasing training sequence length, however, is not the main difficulty; a bigger problem is that this Steiner solution would effectively constrain all the bases to use one and the same fundamental PN sequence and this would not allow addressed units to discriminate between bases by different codes when they are searching for handoff possibilities. Steiner is also inflexible since there is contention over the order in which the bases should cycle their training sequences. This contention will appear at all six interfaces of the base's cell with other cells in a hexagonal layout and so sequence cycling must be set up to be compatible with all adjacent sites that could be affected.
As will now be appreciated, communication systems demand the resolution of multiple channels that combine to provide a composite channel impulse response C from observed training sequences S transmitted by several base stations each with multiple antenna elements. In this regard, a time domain channel estimation h1(t) for pilot sequence s1(t) convolved through channel h(t) that is subject to noise n(t) may be obtained through a time-reversed correlation of the received sequence with a matched filter having the form s1“(−t). In the frequency domain, the channel estimation takes the form Ĥ1(□k).
For the purposes of channel estimation, matched filtering of the PN sequence in the receiver can be performed through fast Fourier Transform (FFT) techniques. If the received data block of N samples is {y1, y2, . . . yN} (which could be a sequence like {s1, s2, . . . sM}), then FFT operation yields;
            {                        s          0                ,                              s            1                    ⁢                                          ⁢          …          ⁢                                          ⁢                      s                          N              -              1                                          }        ⁢          ⇒      FFT        ⁢    S              {                        y          0                ,                              y                          1              ⁢                                                                            ⁢          …          ⁢                                          ⁢                      y                          N              -              1                                          }        ⁢          ⇒      FFT        ⁢    Y  In other words, matched filtering operation in the time domain is equivalent to a point-by-point multiplication in the discrete Fourier domain, namely:{circle around (H)}k(MF)=Yk·Sk*k=0 . . . 2n−1
            H      ^        k          (      MF      )        ⁢      ⇒    IDFT    ⁢      {                            h          ^                0                  (          MF          )                    ,                                    h            ^                    1                      (            MF            )                          ⁢                                  ⁢        …        ⁢                                  ⁢                              h            ^                                N            -            1                                (            MF            )                                }  with two different channel estimates contained in the semi-sequences of {h}, namely:{ĥ}1={ĥ0, ĥ1 . . . ĥN/2−1}{ĥ}2={ĥN/2 . . . {circle around (h)}N−1}Of course, once in the frequency domain, sidelobes present in the frequency spectrum in a channel estimation circuit containing a matched filter may be minimised through the use of a Wiener (least squares) filter technique.
The Wiener least squares filter provides an equalisation technique that utilises a modified inverse filter that controls the white noise response of the filter, i.e. the undesired enhancement of thermal noise from the antenna. The paper “Smart Antennas for Third Generation Mobile Radio Systems” by Martin Haardt (Siemens), Stanford Colloquium on Smart Antennas”, July 1999, describes channel equalisation in terms of the Wiener filtering response, and explores how two or more receiving antennas can be included in the description of the received data. A further paper by H Sari et al titled “Transmission Techniques for Digital IV Broadcasting”, IEEE communications magazine 33(2) February 1995, discusses channel equalisation in the context of the Wiener filter mechanism. Space-time transmit diversity (STTD) is included in the standards for 3G cellular systems in the European UMTS Terrestrial Radio Access (UTRA) system and similar systems in the American CDMA2000 proposals. Consequently, it is important for these STUD systems to implement efficiently data recovery and equalisation algorithms. Utilisation of the Wiener filter in a communication system environment is further discussed in the Applicant's Co-pending European Patent application 01300520.2 (Applicant's reference 11872ID-Hudson) having both a priority claim to U.S. patent application Ser. No. 09/488,721 and corresponding to the continuation-in-part application (Applicant's reference 11872IDUS04I-Hudson) filed in respect thereof, all incorporated herein by reference.
Equalisers and channel whiteners are generally not well conditioned, especially if the channel has zeros or deep minima in its frequency response. The stable minimum mean square error (MMSE) Wiener filter solution for the channel is therefore:
                    H        ^            k              (        W        )              =                            Y          k                ·                  S          k          *                                                                            S              k                                            2                +                  σ          2                                        H        ^            k              (        W        )              ⁢          ⇒      IDFT        ⁢          {                                    h            ^                    0                      (            W            )                          ⁢                                  ⁢        …        ⁢                                  ⁢                              h            ^                                N            -            1                                (            W            )                              }      where σ2 is the variance of the thermal noise level in the frequency domain. If the DFT is orthogonal, i.e. a unitary matrix operation, then this σ2 value is the same as the time domain noise variance per sample, but most FFT algorithms apply some form of scaling for which allowance must be made.
Use of the Wiener fitter solution results in residual errors caused mainly by thermal noise in the sidelobes (and not error due to imperfections in the code sequence); this can be contrasted with PN sequence autocorrelation sidelobes experienced in a matched filter environment.
The paper by J. Blantz et al on the “Performance of a cellular hybrid C/TDMA mobile radio system applying joint detection and coherent receiver antenna diversity”, IEEE J. Selected Areas in Comms. 12(4), May 1994, pp. 568–579, describes a multi-user detection algorithm. A second paper by A. Klein et al on “Zero forcing and minimum mean square error equalisation for multiuser detection in code-division multiple access channels”, IEEE Trans. Veh. Tech. 45(2), May 1996, pp. 276–287, provides further context to the present invention.
In overview therefore, PN training sequences sent from multiple base stations (or Node Bs) employing one or more transmit elements are subject to multipath that results in code cross-correlation and an inability at a receiver to resolve the individual channels and establish the individual channel-specific impulse responses. More specifically, whilst time alignment of base station transmissions may produce a summation of signals at the receiver (when employing appropriate windowing on a chip-by-chip basis with respect to identifiable correlation spikes), the receiver is only able to detect a composite channel impulse response that is unlikely to reflect accurately any of the actual transmission paths. Moreover, whilst the composite channel impulse response may be sufficient in the context of soft handover (in IS-96, for example), the composite channel impulse response is generally insufficient in third generation systems, including systems offering space-time coding. Furthermore, there is a reticence shown by service providers to provide synchronicity between base station transmissions since synchronised transmissions increase infrastructure costs, such as through the necessary provision of an accurate timing reference.